52 citations · 66 across the 7 of their papers we have counts for
7 papers
Vision Language Models are In-Context Value Learners
Yecheng Jason Ma, Joey Hejna, Ayzaan Wahid +15
Predicting temporal progress from visual trajectories is important for intelligent robots that can learn, adapt, and improve. However, learning such progress estimator, or temporal…
Imagined Potential Games: A Framework for Simulating, Learning and Evaluating Interactive Behaviors
Lingfeng Sun, Yixiao Wang, Pin-Yun Hung +4
Interacting with human agents in complex scenarios presents a significant challenge for robotic navigation, particularly in environments that necessitate both collision avoidance a…
Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs
Hao-Tien Lewis Chiang, Zhuo Xu, Zipeng Fu +19
An elusive goal in navigation research is to build an intelligent agent that can understand multimodal instructions including natural language and image, and perform useful navigat…
MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts
Zhuo Xu, Rui Zhou, Yida Yin +3
Data-driven methods have great advantages in modeling complicated human behavioral dynamics and dealing with many human-robot interaction applications. However, collecting massive…
Generative Expressive Robot Behaviors using Large Language Models
Karthik Mahadevan, Jonathan Chien, Noah Brown +6
People employ expressive behaviors to effectively communicate and coordinate their actions with others, such as nodding to acknowledge a person glancing at them or saying "excuse m…
SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities
Boyuan Chen, Zhuo Xu, Sean Kirmani +6
Understanding and reasoning about spatial relationships is a fundamental capability for Visual Question Answering (VQA) and robotics. While Vision Language Models (VLM) have demons…